Journal Article

DKFZ-2026-02118

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.

Sun, J. ; Biharie, K. ; Cai, P. ; Müller-Bötticher, N. ; Kiessling, P. ; Turner, M. A. ; Dam, S. H. ; Heyl, F.DKFZ* ; Kathirchelvan, S. ; Emons, M. ; Gunz, S. ; Twardziok, S. ; El-Heliebi, A. ; Zacharias, M. ; 0 participants, S. 2. (Collaboration Author) ; Eils, R. ; Reinders, M. ; Gottardo, R. ; Kuppe, C. ; Long, B. ; Mahfouz, A. ; Robinson, M. D. ; Ishaque, N. ; Dam, S. H. (Contributor) ; Alakwaa, F. (Contributor) ; Alam, S. (Contributor) ; Calleja, M. (Contributor) ; Chang, Y. (Contributor) ; Chartrand, T. (Contributor) ; Chou, N. S. (Contributor) ; Dong, E. Y. (Contributor) ; Fletcher, M. (Contributor) ; Gavriilidis, G. (Contributor) ; Kanitz, A. (Contributor) ; Kher, S. (Contributor) ; Kümmerle, L. (Contributor) ; Luongo, F. A. (Contributor) ; Mao, Q. (Contributor) ; Moranzoni, G. (Contributor) ; Moreno, M. M. (Contributor) ; Okhtienko, A. (Contributor) ; Perry, L. (Contributor) ; Pfeiferova, L. (Contributor) ; Pikulska, D. (Contributor) ; Prabhakar, S. (Contributor) ; Saghaleyni, R. (Contributor) ; Seferbekova, Z. (Contributor)DKFZ* ; Singhal, V. (Contributor) ; Sitani, D. (Contributor) ; Soneson, C. (Contributor) ; Tiesmeyer, S. (Contributor) ; Varrone, M. (Contributor) ; Wong, S.-H. (Contributor)DKFZ* ; Zaygerman, L. (Contributor) ; Zulueta-Coarasa, T. (Contributor)

2026
Nature Publishing Group London [u.a.]

Abstract: Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects function. However, the reliability of current benchmarks of spatially aware clustering (SAC) methods is undermined by their narrow focus on Visium and brain tissue datasets and the incorrect interpretation of manual annotation as ground truth. Here we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration and metric evaluation, enabling rapid inclusion of new methods and datasets. Our analysis revealed substantial limitations in the generalizability and reproducibility of SAC methods and shows that anatomical labels commonly used as ground truths are often biased, error prone and unsuitable for benchmarking. Rather than ranking methods, we propose a consensus-guided workflow where descriptive spatial metrics highlight high-entropy regions of method disagreement, enabling targeted feedback for tissue experts. Applied to brain and cancer datasets, this approach uncovered biologically meaningful patterns overlooked by individual SAC methods and manual annotations, highlighting the need for iterative, expert-in-the-loop evaluation.

Note: epub

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Contributing Institute(s):

1. Bioinformatik der Genomik und Systemgenetik (B260)
2. Künstl. Intelligenz in der Onkologie (B450)
3. Personalisierte Medizinische Onkologie (A420)
4. Angewandte Bioinformatik (B330)

Research Program(s):

1. 319H - Addenda (POF4-319H) (POF4-319H)

Appears in the scientific report 2026

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Database coverage:
; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; DEAL Nature ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 40 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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